Biomarkers for Diagnosis, Therapy and Prognosis in Colorectal Cancer: a study from databases, machine learning predictions to laboratory confirmations To my parents 献给我的父母 Örebro Studies in Medicine 214 XUELI ZHANG Biomarkers for Diagnosis, Therapy and Prognosis in Colorectal Cancer: a study from databases, machine learning predictions to laboratory confirmations © Xueli Zhang, 2020 Title: Biomarkers for Diagnosis, Therapy and Prognosis in Colorectal Cancer: a study from databases, machine learning predictions to laboratory confirmations Publisher: Örebro University 2020 www.oru.se/publikationer-avhandlingar Print: Örebro University, Repro 05/2020 ISSN 1652-4063 ISBN 978-91-7529-341-7 Abstract Xueli Zhang (2020): Biomarkers for Diagnosis, Therapy and Prognosis in Colorectal Cancer: a study from databases, machine learning predictions to laboratory confirmations. Örebro Studies in Medicine 214. Colorectal cancer (CRC) is one of the leading causes of cancer death worldwide. Early diagnosis and better therapy response have been believed to be associated with better prognosis. CRC biomarkers are considered as precise indicators for the early diagnosis and better therapy response. It is, there- fore, of importance to find out, analyze and evaluate the CRC biomarkers to further provide the more precis evidence for predicting novel potential biomarkers and eventually to improve early di- agnosis, personalized therapy and prognosis for CRC. In this study, we started with creating and establishing a CRC biomarker database. (CBD: http://sys- bio.suda.edu.cn/CBD/index.html) In the CBD database, there were 870 reported CRC biomarkers col- lected from the published articles in PubMed. In this version of the CBD, CRC biomarker data was care- fully collected, sorted, displayed, and analyzed. The major applications of the CBD are to provide 1) the records of CRC biomarkers (DNA, RNA, protein and others) concerning diagnosis, treatment and prog- nosis; 2) the basic and clinical research information concerning the CRC biomarkers; 3) the primary re- sults for bioinformatics and biostatics analysis of the CRC biomarkers; 4) downloading/uploading the biomedicine information for CRC biomarkers. Based on our CBD and other public databases, we further analyzed the presented CRC bi- omarkers (DNAs, RNAs, proteins) and predicted novel potential multiple biomarkers (the combina- tion of single biomarkers) with biological networks and pathways analysis for diagnosis, therapy response and prognosis in CRC. We found several hub biomarkers and key pathways for the diag- nosis, treatment and prognosis in CRC. Receiver operating characteristic (ROC) test and survival analysis by microarray data revealed that multiple biomarkers could be better biomarkers than the single biomarkers for the diagnosis and prognosis of CRC. There are 62 diagnosis biomarkers for colon cancer in our CBD. In the previous studies, we found these present biomarkers were not enough to improve significantly the diagnosis of colon cancer. In order to find out novel biomarkers for the colon cancer diagnosis, we have performed /machine learning (ML) techniques such as support vector machine (SVM) and regression tree to predict candidate to discover diagnostic biomarkers for colon cancer. Based on the protein-protein interaction (PPI) network topology features of the identified biomarkers, we found 12 protein biomarkers which were considered as the can- didate colon cancer diagnosis biomarkers. Among these protein biomarkers Chromogranin-A (CHGA) was the most powerful biomarker, which showed good performance in bioinformatics test and Immuno- histochemistry (IHC). We are now expanding this study to CRC. Expression of CHGA protein in colon cancer was further verified with a novel logistic regression based meta-analysis, and convinced as a valuable diagnostic biomarker as compared with the typical diagnostic biomarkers, such as TP53, KRAS and MKI67. microRNAs (miRNAs/miRs) have been considered as potential biomarkers. A novel miRNA-mRNA interaction network-based model was used to predict miRNA biomarkers for CRC and found that miRNA-186-5p, miRNA-10b-5p and miRNA-30e-5p might be the novel biomarkers for CRC diagnosis. In conclusion, we have created a useful CBD database for CRC biomarkers and provided detailed information for how to use the CBD in CRC biomarker investigations. Our studies have been focus- ing on the biomarkers in diagnosis, therapy and prognosis. Based on our CBD and other powerful cancer associated databases, ML has been used to analyze the characteristics of the CRC biomarkers and predict novel potential CRC biomarkers. The predicted potential biomarkers were further con- firmed at biomedical laboratory. Keywords: biomarkers, diagnosis, therapy response, prognosis, database, machine learning, CRC Xueli Zhang, School of Medical Sciences Örebro University, SE-701 82 Örebro, Sweden, [email protected] Table of Contents LIST OF PUBLICATIONS ........................................................................ 9 OTHER PAPERS NOT IN THIS THESIS ............................................... 10 LIST OF ABBREVIATIONS ................................................................... 11 1 INTRODUCTION ............................................................................... 13 1.1 Colorectal cancer .............................................................................. 14 1.1.1 Colorectal cancer diagnosis .................................................... 15 1.1.2 Colorectal cancer treatment ................................................... 16 1.1.3 Colorectal cancer prognosis ................................................... 18 1.2 Biomarkers ........................................................................................ 19 1.2.1 Biomarkers in colorectal cancer ............................................. 19 1.2.2 Biomarker detection ............................................................... 29 1.3 Bioinformatics approach ................................................................... 30 1.3.1 Biomedicine databases............................................................ 30 1.3.2 Complex network .................................................................. 36 1.3.3 Machine learning ................................................................... 38 1.3.4 Novel meta-analysis ............................................................... 40 2 THE PRESENT INVESTIGATION ..................................................... 42 2.1 Paper I ............................................................................................... 42 2.1.1 Background and aims ............................................................. 42 2.1.2 Materials and methods ........................................................... 42 2.1.3 Results and discussions .......................................................... 42 2.2 Paper II.............................................................................................. 43 2.2.1 Background and aims ............................................................. 43 2.2.2 Materials and methods ........................................................... 43 2.2.3 Results and discussions .......................................................... 43 2.3 Paper III ............................................................................................ 44 2.3.1 Background and aims ............................................................. 44 2.3.2 Materials and methods ........................................................... 44 2.3.3 Results and discussions .......................................................... 44 2.4 Paper IV ............................................................................................ 45 2.4.1 Background and aims ............................................................. 45 2.4.2 Materials and methods ........................................................... 45 2.4.3 Results and discussions .......................................................... 45 2.5 Paper V ............................................................................................. 46 2.5.1 Background and aims ............................................................. 46 2.5.2 Materials and methods ........................................................... 46 2.5.3 Results and discussions ........................................................... 46 ACKNOWLEDGEMENTS ..................................................................... 47 REFERENCES ........................................................................................ 48 List of publications I. Zhang X, Sun X-F, Cao Y, Ye B, Peng Q, Liu X, Shen B and Zhang H CBD: a biomarker database for colorectal cancer. Database 10.1093/database/bay046, 2018 II. Zhang X, Sun X-F, Shen B and Zhang H Potential applications of DNA, RNA and protein biomarkers in diagnosis, therapy and prognosis for colorectal cancer: a study from databases to AI-assisted verification. Cancers 11:172, 2019 III. Zhang X, Zhang H, Fan C-W, Shen B and Sun X-F Loss of CHGA expression as a potential biomarker for colon cancer diagnosis: a study on biomarker discovery by machine learning and confirmation in colorectal cancer tissue microarrays. Submitted, 2020 IV. Zhang X, Zhang H, Shen B and Sun X-F Chromogranin-A expression as a novel biomarker for early diagnosis of colon cancer patients. Int J Mol Sci 20: 2919, 2019 V. Zhang X, Zhang H, Shen B and Sun X-F Novel microRNA biomarkers for colorectal cancer early diagnosis and 5-fluorouracil